Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity

DOI10.1007/s10827-015-0565-5zbMath1382.92102arXiv1502.04993OpenAlexW3121515295WikidataQ35825887 ScholiaQ35825887MaRDI QIDQ1704910

Abigail Morrison, Moritz Deger, Yury V. Zaytsev

Publication date: 13 March 2018

Published in: Journal of Computational Neuroscience (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1502.04993

zbMATH Keywords

inverse problemmaximum likelihood estimationpoint processsparsitygeneralized linear modelnetwork topologypenalized likelihoodspike trainssynaptic connectivityconnectivity inferenceconnectome identification

Mathematics Subject Classification ID

Neural biology (92C20)

Related Items (3)

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology ⋮ Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks ⋮ Inference of synaptic connectivity and external variability in neural microcircuits

Uses Software

• L-BFGS
• LBFGS-B
• Scikit
• Python
• NEST

Cites Work

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• Chebyshev Approximations for the Exponential Integral Ei(x)
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

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